Hedgehog target genes regulate lipid metabolism to drive basal cell carcinoma and medulloblastoma

Hedgehog (Hh) signaling is essential for development, homeostasis, and regeneration1. Misactivation of the Hh pathway underlies medulloblastoma, the most common malignant brain tumor in children, and basal cell carcinoma (BCC), the most common cancer in the United States2. Primary cilia regulate Hh signal transduction3, but target genes that drive cell fate decisions in response to ciliary ligands or oncogenic Hh signaling are incompletely understood. Here we define the Hh gene expression program using RNA sequencing of cultured cells treated with ciliary ligands, BCCs from humans, and Hh-associated medulloblastomas from humans and mice (Fig. 1a). To validate our results, we integrate lipidomic mass spectrometry and bacterial metabolite labeling of free sterols with genetic and pharmacologic approaches in cells and mice. Our results reveal novel Hh target genes such as the oxysterol synthase Hsd11β1 and the adipokine Retnla that regulate lipid metabolism to drive cell fate decisions in response to Hh pathway activation. These data provide insights into cellular mechanisms underlying ciliary and oncogenic Hh signaling and elucidate targets to treat Hh-associated cancers.

The Hh gene expression program is well studied in medulloblastoma 21-23 , but Hh target genes in newly diagnosed human BCC are incompletely understood. To address this limitation in our understanding of oncogenic Hh signaling, RNA sequencing was performed on matched tumor and adjacent skin samples from 10 patients with newly diagnosed BCC. Differential expression and ontology analyses demonstrated alterations in cell development, lipid metabolism, and diverse biosynthetic pathways in BCC compared to adjacent skin (Fig. 2a, b and Supplementary Table 4). Lipidomic mass spectrometry on the same 10 pairs of matched BCC and adjacent skin samples showed enrichment of cilia-associated and Smo-activating sterols and oxysterols in BCC compared to adjacent skin, including cholesterol, desmosterol, 7-keto,27hydroxycholesterol (7k,27-OHC), 24k-cholesterol (24k-C), and 24(S),25-EC 10 (Fig. 3a). 7-keto-cholesterol (7k-C), a precursor of 7k,27-OHC and 7β,27-DHC that can be catabolized to 7-hydroxycholesterol  by the oxysterol synthase Hsd11β1 24 , was also enriched in BCC compared to adjacent skin, suggesting that the Hh target gene Hsd11β1 from NIH3T3 cells (Fig. 1g, h and Supplemental Table 3) may be suppressed in BCC. In support of this hypothesis, comparison of differentially expressed genes in human BCC ( Fig. 2 Table 4) to differentially expressed Hh target genes in NIH3T3 cells ( Fig. 1 and Supplementary Table 3) revealed Hsd11β1 was suppressed but canonical Hh target genes (Gli1, Ptch1) were enriched in human BCC (Fig. 3b).

and Supplementary
These data suggest that Hsd11β1 regulates lipid metabolism to inhibit Hh signal transduction, and that Hsd11β1 suppression in BCC facilitates oncogenic Hh signaling to drive cancer cell proliferation. To test this hypothesis, NIH3T3 cells stably expressing dCas9-KRAB CRISPR interference (CRISPRi) machinery 25 were transduced with sgRNAs targeting Hsd11β1 (sgHsd11β1) or non-targeted control sgRNAs (sgNTC). Hsd11β1 suppression did not in uence Smo accumulation in cilia but enhanced canonical Hh target gene expression in NIH3T3 dCas9 − KRAB cells compared to sgNTC (Fig. 3c-e). NIH3T3 cells stably expressing a doxycycline-inducible FLAG-Hsd11β1 construct showed no change in ciliary Smo accumulation with Hsd11β1 over-expression, but Hsd11β1 over-expression attenuated canonical Hh target gene expression in NIH3T3 dCas9 − KRAB cells compared to vehicle control (Fig. 3f-h). Hsd11β1 over-expression in ASZ BCC cells suppressed Smo-activating sterols as measured using perfringolysin O (PFO*), a bacterial metabolite that labels free sterol lipids in live cells 15 (Fig. 3i, j). Hsd11β1 did not localize to ASZ cilia (Fig. 3k), but over-expression of Hsd11β1 attenuated clonogenic growth of BCC cells (Fig. 3l). Hsd11β2 opposes Hsd11β1 and activates the Hh pathway by converting 7-hydroxycholesterol (7-OHC) to 7k-C 24 , and carbenoxolone (CNX), a naturally occurring small molecule that blocks Hhassociated medulloblastoma 12,26 , inhibits Hsd11β2. In ASZ BCC cells, CNX attenuated clonogenic growth and synergized with Hsd11β1 over-expression (Fig. 3l). These data demonstrate Hsd11β1 regulates Smoactivating lipid metabolism to inhibit Hh signal transduction and block cell proliferation in vitro.
Cilia-associated sterol and oxysterol lipids drive Hh-associated medulloblastoma growth and response to treatment 12,26 . To identify Hh target genes underlying these phenotypes, differentially expressed Hh target genes from NIH3T3 cells ( Fig. 1 Table 6). Hsd11β1 was enriched in Hh-associated medulloblastomas compared to Group 3 medulloblastomas from humans (Fig. 4a). Hsd11β1 was suppressed in Math1-Cre SmoM2 c Hhassociated medulloblastomas compared to control cerebella from mice (Fig. 4b), as was the case for Hsd11β1 expression in human BCCs compared to adjacent skin (Fig. 2 and Supplementary Table 4). These data suggest Hh target are enriched in Hh-associated cancers compared to related cancers arising in similar anatomic locations, but that Hh target genes that inhibit oncogenic Hh signaling, like Hsd11β1, are suppressed in Hh-associated cancers compared to adjacent tissues, like the skin or cerebellum.
Retnla, an adipokine that regulates sterol synthase expression to control lipid metabolism 27 , was enriched in NIH3T3 cells after treatment with ciliary ligands (Fig. 1g, h and Supplemental Table 3 (Fig. 4c-e). Moreover, Retnla suppression reduced the expression of Hsd11β2 (Fig. 4f), an oxysterol synthase and positive regulator of the Hh pathway that produces Smo-activating lipids 12 . In support of these data, PFO* labeling was reduced in NIH3T3 dCas9 − KRAB cells expressing sgRetnla compared to sgNTC (Fig. 4g). NIH3T3 cells stably expressing a doxycycline-inducible FLAG-Retnla construct showed increased ciliary Smo accumulation with Retnla overexpression and increased canonical Hh target gene expression compared to vehicle control ( Fig. 4hj). Retnla overexpression increased Hsd11β2 expression (Fig. 4k), and increased PFO* labeling in NIH3T3 cells (Fig. 4l) and Hh-associated DAOY medulloblastoma cells (Fig. 4m, n). Like Hsd11β1 in ASZ cells ( Fig. 3k) Retnla did not localize to DAOY cilia (Fig. 4o), but over-expression of Retnla increased clonogenic growth in a manner that was attenuated by the Hsd11β2 antagonist CNX (Fig. 4p). These data demonstrate Retnla regulates Smo-activating lipid metabolism to activate Hh signal transduction and drive cell proliferation in vitro.
Human iPSC-derived neuroepithelial stem (NES) cells overexpressing MYCN (NES MYCN ) can be implanted into the cerebella of mice to model Hh-associated medulloblastoma 28 , and we found NES MYCN cell express primary cilia (Fig. 5a, b). To determine if Hh target genes regulating Smo-activating lipid metabolism also regulate oncogenic Hh cell fate decisions in vivo, doxycycline-inducible HSD11β1 or Retnla were stably expressed in NES MYCN cells (Fig. 5c). NES MYCN cells were implanted into the cerebella of Foxn1 nu (Nu/Nu) mice. Tumor initiation and growth were monitored using non-invasive intracranial bioluminescence. HSD11β1 overexpression attenuated Hh-associated medulloblastoma tumorigenesis (3 of 12 mice, 25%) compared to Retnla overexpression (12 of 13 mice, 92%) or NES MYCN control cells without overexpression of Hh target genes (24 of 25 mice, 96%) (Fig. 5d). Moreover, survival was improved in iPSC-derived NES MYCN Hh-associated medulloblastomas with HSD11β1 overexpression compared to control or Retnla overexpression conditions (Fig. 5e). Histological analysis of hematoxylin and eosin-stained sections at survival endpoints showed NES MYCN control medulloblastomas were comprised of hyperchromatic tumor cells with nuclear molding, indistinct to multiple small nucleoli, and indistinct cytoplasm (Fig. 5f). Abundant Homer-Wright (neuroblastic) rosettes were admixed with areas of neuropil. Tumor cells showed increased pale cytoplasm or mature neuronal phenotypes. NES MYCN Hhassociated medulloblastomas with Retnla overexpression showed a similar appearance, but with fewer rosettes and increased neuropil-like stroma (Fig. 5f). HSD11β1 overexpression in NES MYCN Hh-associated medulloblastoma was associated with prominent central nucleoli and vesicular chromatin, with rare rosettes and no areas of extensive neuropil-like stroma (Fig. 5f). To elucidate the gene expression programs underlying phenotypic differences across iPSC-derived NES MYCN Hh-associated medulloblastomas, RNA sequencing was performed on control tumors (n = 3), tumors with HSD11β1 overexpression (n = 3), and tumors with Retnla overexpression (n = 3) (Supplementary Table 7). Differential expression and ontology analyses demonstrated enrichment in cell signaling and cell adhesion programs in medulloblastomas with overexpression of Retnla, and suppression of ciliary organization and Hh cell fate programs (e.g. endochondral ossi cation) in medulloblastomas with overexpression of HSD11β1 (Fig. 5g).
In summary, we de ne the Hh gene expression program using RNA sequencing of (1) NIH3T3 cells treated  Table 5), and (4) in vivo genetic and iPSC models of Hhassociated medulloblastoma in mice (Fig. 4b, 5g and Supplementary Table 5, 6). Our results reveal a core Hh gene expression program comprised of 143 protein coding genes that are associated with lipid synthesis (Hsd11β1, Retnla), metabolism, cell signaling, cell adhesion, or angiogenesis (Supplemental Table 3). To validate our results, we integrate mechanistic and functional studies with lipidomic mass spectrometry, bacterial metabolite labeling of free sterols, and genetic and pharmacologic approaches in cells and mice. We show the Hh target gene Hsd11β1 regulates Smo-activating lipid metabolism to inhibit Hh signal transduction and block cell proliferation in vitro, and that the Hh target gene Retnla regulates Smo-activating lipid metabolism to activate Hh signal transduction and drive cell proliferation in vitro. In vivo, Hsd11β1 attenuates Hh-associated medulloblastoma tumorigenesis and improves survival, reprogramming the cellular architecture and gene expression of the most common malignant brain tumor in children. These data provide insights into cellular mechanisms underlying ciliary and oncogenic Hh signaling and elucidate targets to treat Hh-associated cancers.
Several key questions remain regarding lipid metabolism and Hh signal transduction. First, Hsd11β1 inhibits Hh target gene expression (  [1][2][3] . Second, Hsd11β1 expression is increased in NIH3T3 cells in response to ciliary ligands but is suppressed in Hh-associated cancers (Fig. 3b, 4b); and reminiscent of Ptch1, Hsd11β1 is both a target and a negative regulator of the Hh pathway. Thus, we propose Hsd11β1 expression represents a negative feedback loop regulating Hh signaling output that can be disrupted to drive the growth of Hh-associated cancers. Third, Hsd11β1 and Retnla regulate Hh signal transduction, at least in part, through Hsd11β2

Code availability
The following open-source software, tools, and packages were used for data analysis in this study: were differentiated into NES cells as previously described 28  For clonogenic assays, 1000 cells were seeded in 10 cm 2 plates and cultured for 10 days. Cells were xed in methanol for 30 minutes and stained with 0.01% crystal violet (C6158, Sigma-Aldrich) for 1 hour. Fluorescence microscopy was performed on a LSM 800 confocal laser scanning microscope (Zeiss). Acquisition parameters were controlled using Zeiss Zen v2.3. Images were collected at room temperature (25°C) using a Plan-APOCHROMATIC 63x/1.4 oil immersion objective.
Ciliary Smo intensity was quanti ed from 2D maximum projections of 16-bit TIFF images, and Smo signal (SS) was measured by tracing the cilia using Arl13b as a marker in ImageJ. Background was subtracted by averaging the equivalent measurement in the regions immediately adjacent to each cilium (AL and AR) for a nal formula of (SS -(AL + AR)/2). Ciliary length was also measured in ImageJ, and ciliary prevalence was quanti ed as the percentage of nucleated cells expressing primary cilia.
Staining with PFO*-647 for immuno uorescence, as opposed to ow cytometry as described above, was performed using cells that were seeded onto glass coverslips and blocked in 4°C PBS supplemented with 10mg/mL BSA for 15 minutes. PFO*-647 was diluted in blocking buffer (1:45) and incubated with cells and biological triplicates, this yielded a total of 72 individual NIH3T3 cultures that were analyzed using RNA sequencing. RNA was isolated from cultured cells as described above for QPCR but was prepared for RNA sequencing using the Illumina TruSeq stranded mRNA kit (20020594, Illumina). Brie y, this kit enriches for mRNA using poly-T beads and ligates sequencing adaptors to cDNA for next generation sequencing. Samples were sequenced on an Illumina NovaSeq with a depth of least 25 million 100 bp paired-end reads per sample. Sequencing reads were processed using Trimmomatic 34 to remove leading and trailing bases with quality scores below 28 as well as bases that did not have an average quality score of 28 within a sliding window of 4 bases. Any reads shorter than 75 bases after trimming were removed. Reads were subsequently mapped to the mouse reference genome GRCm38.p6 using HISAT2 with default parameters 35,36 , resulting in ~90% of reads mapping to annotated genes for each sample.
For differential expression analysis, exon level count data were extracted from mapped HISAT2 data using featureCounts 37 . Differential expression analysis was performed in R using DESeq2 38 with the 'apeglm' parameter to calculate log fold changes after setting a false discovery rate of 0.05 38 . Differentially expressed genes were identi ed as those with log 2 fold changes ≥1 and adjusted p-value ≤0.05 (Supplementary Table 1 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were trimmed with Trimmomatic to remove leading and trailing bases with quality scores below 28, and any bases that did not have an average quality score of 28 within a sliding window of 4 bases. Any reads shorter than 110 bases after trimming were removed. Reads were mapped to the human reference genome GRCh38 using HISAT2 with default parameters. For downstream expression analysis, extracted exon level count data were extracted from mapped HISAT2 output using featureCounts. Differential expression analysis was performed in R with DESeq2, using the 'apeglm' parameter to accurately calculate log fold changes and setting a false discovery rate of 0.05. Differentially expressed genes were identi ed as those with log 2 fold changes ≥1 and adjusted p-value ≤0.05 (Supplementary Table 4).
RNA sequencing and analysis of iPSC-derived NES MYCN Hh-associated medulloblastomas was performed as described above. Tumors were isolated from mice by dissection, and mechanically lysed using a TissueLyser II (Qiagen) according to the manufacturer's instruction. RNA was isolated as described above for QPCR, and was similarly prepared for RNA sequencing using the Illumina TruSeq stranded mRNA kit. Samples were sequenced on an Illumina NovaSeq with a depth of least 25 million 100 bp paired-end reads per sample. Sequencing reads were processed using Trimmomatic 34 to remove leading and trailing bases with quality scores below 28 as well as bases that did not have an average quality score of 28 within a sliding window of 4 bases. Any reads shorter than 75 bases after trimming were removed. Reads were subsequently mapped to the mouse reference genome GRCh38.p6 using HISAT2 with default parameters 35,36 , resulting in ~90% of reads mapping to annotated genes for each sample. Differential expression analysis was performed in R with DESeq2, using the 'apeglm' parameter to accurately calculate log fold changes and setting a false discovery rate of 0.05. Differentially expressed genes were identi ed as those with log 2 fold changes ≥1 and adjusted p-value ≤0.05 (Supplementary Table 7).
Pathway analyses for basal cell carcinoma and NES medulloblastoma data set were performed using Gene Set Enrichment Analysis (GSEA) with preranking to determine if differentially expressed genes belonged to common biological pathways 39 . Gene rank scores were calculated using the formula: sign(log2FC) × −log10(P). Pathways were de ned using Human_GOBP_AllPathways_no_GO_iea_December_01_2022_symbol.gmt, a gene set le that is regularly maintained and updated by the Bader laboratory. Positive and negative enrichment les were obtained by carrying out 2000 permutations. A pathway enrichment map was generated using EnrichmentMap in Cytoscape to visualize pathway analysis results. Nodes with FDR q-value ≤0.05, p-value ≤0.05, and nodes sharing gene overlaps with Jaccard + Overlap Combined (JOC) threshold of 0.375 were connected by blue lines (edges) to generate network maps. Clusters of related pathways were identi ed and annotated using AutoAnnotate in Cytoscape, which relies on a Markov Cluster algorithm that connects pathways by shared keywords in the description of each pathway. The resulting groups of pathways were designated as consensus pathways that are annotated in circles in the gures.

Mouse xenografts
Immunocompromised Nu/Nu 6-8-week old female mice (002019, The Jackson Laboratory) were used for in vivo experiments that were performed in the UCSF Helen Diller Animal Facility. All experiments were performed in accordance with institutional policy and with approval from UCSF IACUC (AN191840). Differentiated NES cells were transduced with a pCDH-CAG-3xFLAG-MYCN-mScarlet-Luciferase lentiviral construct (NES MYCN ). NES MYCN cells were transduced with either pLV-TreGS-Hsd11b1-FLAG or pLV-TreGS-Retnla-FLAG and selected using 2µg/mL puromycin. Construct expression was induced with doxycycline in vitro at 2.5µg/mL and in vivo at 100µg/mL (Sigma-Aldrich, D9891). For in vivo induction, doxycycline water was changed every 2-3 days. Cerebellar NES medulloblastomas were generated by injecting 300,000 cells in 5µL of NES cell medium using stereotactic surgical equipment with the following coordinates: lambda 2mm right, 2mm down, and 2mm deep. Tumor growth was monitored with bioluminescence imaging on an IVIS Spectrum imager (PerkinElmer). For BLI monitoring, mice were injected with 100µL of 30mg/mL D-luciferin diluted in sterile water (LUCK, Goldbio) and imaged after 15 minutes. Quanti cation of bioluminescence readings were performed using LivingImage. Mice were euthanized at pre-determined humane endpoints for intracranial tumor growth, such as cachexia, head tilt, or respiratory distress.

Histology and light microscopy
For iPSC-derived NES MYCN Hh-associated medulloblastomas, depara nization and rehydration of 5µm formalin-xed, para n-embedded (FFPE) tissue sections and H&E staining were performed using standard procedures. All histological experiments were imaged on a BX43 light microscope (Olympus) and analyzed using the Olympus cellSens Standard Imaging Software package.

Statistics
All experiments were performed with independent biological replicates and repeated, and statistics were derived from biological replicates. Biological replicates are indicated in each gure panel or gure legend.
No statistical methods were used to predetermine sample sizes, but sample sizes in this study are similar or larger to those reported in previous publications. Data distribution was assumed to be normal, but this was not formally tested. Investigators were blinded to conditions during clinical data collection and analysis of mechanistic or functional studies. Bioinformatic analyses were performed blind to clinical features, outcomes, or molecular characteristics. The clinical samples used in this study were retrospective and nonrandomized with no intervention, and all samples were interrogated equally. Thus, controlling for covariates among clinical samples was not relevant. Cells and animals were randomized to experimental conditions. No clinical, molecular, or cellular data points were excluded from the analyses. Lines represent means, and error bars represent standard error of the means. Results were compared using Student's t-tests, which are indicated in gure panels or gure legends alongside approaches used to adjust for multiple comparisons. In general, statistical signi cance is shown using asterisks (*p≤0.05, **p≤0.01, ***p≤0.0001), but exact p-values are provided in gure panels or gure legends when possible. Figure 1 The     The Hedgehog target gene Retnla regulates lipid metabolism to activate Hedgehog signaling. a, Differentially expressed genes with log 2 fold change ≥1 intersecting between human Hh-associated medulloblastomas (Supplementary Table 5) and NIH3T3 cells ( Fig. 1 and Supplementary Table 3). b, Differentially expressed genes with log 2 fold change ≥1 intersecting between mouse Hh-associated medulloblastomas (Supplementary Table 6) and NIH3T3 cells ( Fig. 1 and Supplementary Table 3). c, medulloblastomas (n=3) using RNA sequencing. Nodes represent pathways and edges represent shared genes between pathways (p≤0.05, FDR≤0.05). Red nodes are enriched and blue nodes are suppressed.

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download. DaggubatiNatCellBiolEDFigv7.docx DaggubatiNatCellBiolSupplementaryTablesv7.xlsx